Computer Science – Information Theory
Scientific paper
2007-10-10
Computer Science
Information Theory
12 pages, 19 graphic files (15 figures using subfigures), submitted to IEEE Trans. Inform. Theory
Scientific paper
This paper addresses the problem of coding a continuous random source correlated with another source which is only available at the decoder. The proposed approach is based on the extension of the channel coding concept of syndrome from the discrete into the continuous domain. If the correlation between the sources can be described by an additive Gaussian backward channel and capacity-achieving linear codes are employed, it is shown that the performance of the system is asymptotically close to the Wyner-Ziv bound. Even if such an additive channel is not Gaussian, the design procedure can fit the desired correlation and transmission rate. Experiments based on trellis-coded quantization show that the proposed system achieves a performance within 3-4 dB of the theoretical bound in the 0.5-3 bit/sample rate range for any Gaussian correlation, with a reasonable computational complexity.
No associations
LandOfFree
Distributed Source Coding Using Continuous-Valued Syndromes does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Distributed Source Coding Using Continuous-Valued Syndromes, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Distributed Source Coding Using Continuous-Valued Syndromes will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-462604